WO2017057022A1

WO2017057022A1 - Biological state estimation device, biological state estimation method, and computer program

Info

Publication number: WO2017057022A1
Application number: PCT/JP2016/077274
Authority: WO
Inventors: 藤田　悦則; 堀川　正博
Original assignee: デルタ工業株式会社
Priority date: 2015-09-28
Filing date: 2016-09-15
Publication date: 2017-04-06

Abstract

The present invention estimates the fundamental physical condition of a person. The present invention calculates, for each predetermined determination time set in advance, each of a plurality of indicators indicating changes in the state of a bioregulation functional element, including indicators caused by fluctuation highly correlated with brain function, autonomic nervous function, body/mind fatigue, or sensation, said bioregulation functional element obtained from a bioregulation functional element determination means 200, and ascertains a time series variation thereof. Each time series variation is analyzed in order of priority by a fundamental physical condition estimating means 300. The physical condition can thereby be ascertained from a basic fluctuation performance of a human regulation system corresponding to a fundamental physical condition estimation time which is set so as to be longer than the determination time of each bioregulation functional element.

Description

Biological state estimation device, biological state estimation method, and computer program

The present invention relates to a biological state estimating device, a biological state estimating method, and a computer program for estimating a basic physical condition of a person based on a human biological signal measured by a biological signal measuring device.

In the patent documents 1 to 5 and the like, the present inventors have proposed a technique for detecting a vibration generated on the body surface of the back in a human upper body with a biological signal measuring device and analyzing a human state. Sound / vibration information detected from the heart and aorta motion detected from the upper back of a person is pressure vibration generated from the heart and aorta motion. Information on ventricular systole and diastole, circulation auxiliary pump, It includes elasticity information of the blood vessel wall and reflected wave information. That is, the back body surface pulse wave (including Aortic Pulse Wave (APW)) around 1 Hz generated on the back surface from the motion of the heart and aorta, and the sound transmitted to the back side with the heartbeat (“pseudo heart sound” (this specification) The book includes information on the heart sound, which is a heart sound collected from the chest side, as a “pseudo heart sound”). The signal waveform associated with heart rate variability includes sympathetic nervous system and parasympathetic nervous system nerve activity information, and the signal waveform associated with aortic oscillation includes sympathetic nerve activity information.

In Patent Document 1, slide calculation is performed by applying a predetermined time width to a time series waveform of a back body surface pulse wave (APW) near 1 Hz extracted from a collected biological signal (sound / vibration information), and a frequency slope is calculated. A time-series waveform is obtained, and the biological state is estimated from the tendency of the change depending on, for example, whether the amplitude tends to be amplified or attenuated. In addition, the biological signal is subjected to frequency analysis, and the power spectrum of each frequency corresponding to the function adjustment signal, fatigue acceptance signal, and activity adjustment signal belonging to a predetermined ULF band (very low frequency band) to VLF band (very low frequency band). And determining the state of a person from the time series change of each power spectrum. Since the fatigue acceptance signal indicates the degree of progress of fatigue in the normal activity state, in addition to this, by comparing the degree of dominance of the power spectrum of the function adjustment signal and the activity adjustment signal, Status, parasympathetic dominant status, etc.). In addition, the distribution rate of each frequency component is obtained in a time series when the sum of the power spectrum values of the frequency components corresponding to these three signals is set to 100, and the human condition is obtained using the time series change of the distribution rate. Is also disclosed.

Patent Document 2 proposes a technique for displaying a biological state as a physical condition map and a sensory map as a quantification method of the biological state. This is a frequency analysis of the APW described above, and for the analysis section to be analyzed, the analysis waveform is shown in a logarithmic axis display, the analysis waveform is divided into a low frequency band, a middle frequency band, and a high frequency band, and the divided analysis waveforms The analysis waveform is scored based on a certain standard from the slope and the shape of the entire analysis waveform, and plotted on the coordinate axes. The physical condition map is a state in which the state of control of the autonomic nervous system is viewed as a balance between the sympathetic nerve and the parasympathetic nerve, and the sensory map is obtained by superimposing the state of change of heartbeat variability on the physical condition map.

Patent Documents 3 to 5 disclose means for determining the homeostasis maintenance function level. Consistency maintenance function level judgment means uses positive / negative of differential waveform of frequency gradient time series waveform, positive / negative of integral waveform integrating frequency gradient time series waveform, frequency gradient time series waveform using zero cross method and peak detection method The determination is made using at least one of the absolute values of the frequency gradient time series waveforms obtained by performing absolute value processing on the frequency gradient time series waveforms. Based on these combinations, the level of the homeostasis maintenance function is determined. For example, using a frequency gradient and an integral value, it is determined as “constancy maintaining function level 1” when it is equal to or greater than a predetermined value, or the differential value is equal to or smaller than a predetermined value and “ In the case of “peak advantage”, it can be set to be determined as “constancy maintaining function level 4”. These combinations, threshold values for determination, and the like are determined by statistically processing data of a large number of subjects. In addition, the level of the homeostasis maintenance function is divided into, for example, 5 to 7 stages, from the case where the homeostasis maintenance function is excellent (when concentration is high, etc.) to the case where the homeostasis maintenance function is inferior (when overstressed, Decrease of concentration due to driving aside) When displaying on the monitor, the level of 5 to 7 levels is displayed in characters, or if it is higher than the intermediate level (normal state), it is judged that the homeostasis maintenance function is excellent at once. In other cases, it is determined that the homeostasis maintenance function is inferior collectively, and the monitor is set so that different color displays are made.

In Non-Patent Document 1, regarding fingertip plethysmogram information, a frequency gradient time series waveform of a power value reflecting sympathetic nerve information is obtained, and an integral value obtained by processing the absolute value is plotted as a fatigue level in time series. Thus, a technique for drawing a fatigue curve and capturing muscle fatigue is disclosed. Non-Patent Document 2 discloses a technique for capturing muscle fatigue by computing a biological signal obtained from a human back using an air pack sensor and drawing a fatigue curve using a similar method. That is, the state of muscle fatigue can be grasped by using a frequency gradient time-series waveform of power values reflecting sympathetic information (in the case of APW, a frequency gradient time-series waveform by the zero cross method).

JP 2011-167362 A JP 2012-239480 A WO2011 / 046178 JP 2014-117425 A JP 2014-223271 A

Each of the above-described techniques is to determine each person's state by analyzing each element caused by fluctuations related to the bioregulatory function. The timing of the sign is determined, the degree of fatigue is determined, the change in the homeostasis maintenance function level is determined, and the purpose is determined. However, these are all output separately. In Patent Literature 4, time-series changes of a plurality of indicators related to each element caused by fluctuations in the biological regulation function, such as a sleep onset symptom phenomenon, an imminent sleep phenomenon, a low running state, a homeostasis maintenance function level, an initial fatigue state, and mood determination However, in any case, the time-series change of each index is individually determined.

Although obtaining various indexes as in the technique disclosed in Patent Document 4 is very important for more accurately grasping the state of a person, particularly the state of a driver, In technology, since the focus is on using indices related to the autonomic nervous system to more accurately output the state of the corresponding person, the extraction time (determination time) of the biological signal to be determined ) Is relatively short, and the determination result is output at a frequency of once every several tens of seconds to several minutes. However, for example, in driving, a driver or a manager who remotely monitors the information, a basic physical condition (dominant, representative or average in that time zone) over a relatively long time such as 30 minutes, 1 hour. There is also a request to get a general understanding of the physical condition. In other words, for example, it was driven for 1 hour, but during that time, it can basically be estimated that it was driven under good physical condition, or it can be helped by the bioregulatory function that maintains homeostasis in a poor physical condition If it is possible for the driver to grasp roughly whether he / she was driving, he / she can encourage self-determination such as taking a break early.

The present invention has been made in view of the above points, and a plurality of pieces of information on time-series changes of each element (bioregulatory function element) resulting from fluctuations for each bioregulatory function determined one after another in a relatively short time. Use and process this under specified conditions to output the basic physical condition of the person at the basic physical condition estimation time set for a longer time, making it easier for the driver to grasp the general physical condition It is an object to provide a biological state estimation device, a biological state estimation method, and a computer program.

In order to solve the above-mentioned problems, the present inventor diligently studied and came to complete the present invention paying attention to the following points. That is, sound / vibration information generated from the motion of the heart and aorta detected from the upper back of a person, in particular, the back body surface pulse wave (APW) in the vicinity of 1 Hz among them is the elasticity information of the blood vessels and the reflected wave. Contains information. For this reason, by analyzing the back body surface pulse wave (APW) and obtaining a frequency gradient time-series waveform described later, it is possible to obtain information on the overall fluctuation of the living body which is a bioregulatory functional element. In this fluctuation information, if the state of fluctuation for each frequency is captured, the distribution rate for each frequency takes the fluctuation method reflecting the fluctuation element of the brain wave (θ wave, α wave, β wave). By analyzing using the series waveform, fluctuation information such as which frequency band of the electroencephalogram is the dominant fluctuation can be captured. In addition, while the frequency-slope time-series waveform by the zero-cross method is under the control of the autonomic nervous system, the frequency-sequential time-series waveform by the zero-cross method is frequency-analyzed. The function adjustment signal typified by 0.0017 Hz, the fatigue acceptance signal typified by 0.0035 Hz, and the activity spectrum signal typified by 0.0053 Hz are represented by power spectrum ratios of three frequency components. In other words, the distribution rate of the three frequency components representing the shape of the power spectrum obtained by frequency analysis can be considered as a site showing the expression of endocrine function rather than an autonomic nervous reaction. The frequency gradient time series waveform obtained by the zero cross method indicates the degree of sympathetic nerve expression by performing absolute value processing, and the frequency gradient time series waveform obtained by the peak detection method indicates the degree of parasympathetic nerve expression. Therefore, by using these, it is possible to capture the expression of the biological regulation function in more detail. For example, an absolute value process is performed on a frequency gradient time series waveform obtained by the zero cross method, and by integrating this, a fatigue curve indicating the degree of human fatigue is obtained, and the state of muscle fatigue can be grasped. Further, by using at least one or more of positive / negative of the differential waveform of each frequency gradient time series waveform using the zero cross method, the absolute value of each frequency gradient time series waveform of the zero cross method or the peak detection method, etc. It is possible to capture changes in the maintenance function level. The time series waveform of the fatigue curve and the homeostasis maintenance function level is also derived from the frequency gradient time series waveform, and is an index reflecting information on fluctuations in the autonomic nervous system, brain function, and the like. In addition to this, indicators that are highly relevant to physical and mental fatigue (physical condition map, sensory map), and indicators that are highly relevant to sensation (attention level of homeostasis function level, frequency of the level equivalent to warning) The frequency at which a sense of fatigue or fatigue that is required as fatigue is required is also obtained. These capture the fluctuations of each function of the brain, autonomic nervous system, and endocrine system from multiple viewpoints, and statistically process these multiple types of fluctuation indices as described below. It is considered that the basic physical condition of the person to be analyzed can be estimated.

That is, the biological state estimation device of the present invention is a biological state estimation device that analyzes a biological signal of a person measured by a biological signal measurement device and estimates a biological state, and analyzes the biological signal to obtain a brain function. A plurality of indicators indicating fluctuations in the state of the bioregulatory functional element, including indicators caused by fluctuations that are highly related to autonomic nervous function, physical / mental fatigue, or sensation, for each predetermined determination time set in advance Corresponding to a basic physical condition estimation time set longer than each determination time of each of the biological adjustment functional elements in the biological adjustment functional element determination means, the biological adjustment functional element determination means for calculating and calculating the time series change, Basic physical condition estimating means for estimating a basic physical condition of the person, and the level of the basic physical condition of the person estimated by the basic physical condition estimating means, the predetermined basic physical condition estimation time Basic physical condition output means for outputting every time, and the basic physical condition estimation means presets each time series change relating to the state of each of the bioregulatory functional elements determined by the bioregulatory functional element determination means And a means for estimating the basic physical condition of the person at the predetermined basic physical condition estimation time in accordance with a predetermined criterion.

The basic physical condition estimating means includes a time series change of the bioregulatory functional element having a high priority among the time series changes related to each bioregulatory functional element determined by the bioregulatory functional element determining means, and a predetermined reference The first basic physical condition estimating means for estimating the basic physical condition of the person at the predetermined basic physical condition estimation time as a predetermined level, and an estimation target in the first basic physical condition estimating means If not, using the time-series change related to other bioregulatory function elements having a lower priority than the bioregulatory function elements used in the first basic physical condition estimating means, the basic physical condition of the person is determined in advance. Second basic physical condition estimating means for estimating a predetermined level classified based on the criteria of the basic physical condition output means, wherein the basic physical condition output means is the first basic physical condition estimating means or the second It is preferable to adopt a configuration for outputting the level of the person basic physical condition estimated by the foundation basis physical condition estimation means.

The first basic physical condition estimating means includes an index highly relevant to the autonomic nerve function or the physical / mental fatigue among the plurality of biological adjustment functional elements determined by the biological adjustment functional element determination means. Using a time-series change of a highly relevant index, and when the time-series change satisfies a predetermined standard, the basic physical condition level of the person may be estimated as “good” or “bad” preferable.
The first basic physical condition estimating means estimates the basic physical condition of the person as “bad” when a time-series change of an index highly relevant to the autonomic nervous function satisfies a predetermined criterion, Preferably, when the time series change of the index highly related to the physical / mental fatigue satisfies a predetermined standard, it is a means for estimating the basic physical condition of the person as “good”.

The second basic physical condition estimation means includes a plurality of the biological adjustment functional elements determined by the biological adjustment functional element determination means when the first basic physical condition estimation means does not estimate "good" or "bad" Among them, from a time-series change of an index highly relevant to the sense, the basic physical condition level of the person is estimated as either “good”, “bad” or their “intermediate state”. Preferably there is.

The biological state estimation method of the present invention is a biological state estimation method for estimating a biological state by analyzing a human biological signal measured by a biological signal measurement device using a computer, and analyzing the biological signal, Predetermined predetermined judgments for each of a plurality of indicators showing fluctuations in the state of the bioregulatory functional elements, including indicators caused by fluctuations that are highly related to brain function, autonomic nervous function, physical / mental fatigue, or sensation Corresponding to the biological condition functional element determination procedure that calculates every time and obtains its time-series change, and the basic physical condition estimation time that is set longer than each determination time of each biological adjustment functional element in the biological adjustment function element determination procedure A basic physical condition estimation procedure for estimating the basic physical condition of the person, and a level of the basic physical condition of the person estimated by the basic physical condition estimation procedure, the predetermined basic physical condition A basic physical condition output procedure that outputs every time the estimated time of the physical condition elapses, and the basic physical condition estimation procedure is a time-series change related to the state of each of the bioregulatory function elements obtained by the bioregulatory function element determination procedure Is a procedure for analyzing the human body according to a preset priority order and estimating the basic physical condition of the person at the predetermined basic physical condition estimation time in accordance with a predetermined standard.

The basic physical condition estimation procedure of the biological condition estimation method includes: a time series of the biological regulation function element having a high priority among the time series changes related to each biological regulation function element obtained by the biological regulation function element determination procedure. A first basic physical condition estimating procedure for estimating a basic physical condition of the person at the predetermined basic physical condition estimation time as a predetermined level when the change satisfies a predetermined standard; and the first basic physical condition When it is not an estimation target in the estimation procedure, the human basis is used by using the time-series change related to another bioregulatory function element having a lower priority than the bioregulatory function element used in the first basic physical condition estimation procedure. A second basic physical condition estimation procedure for estimating a physical condition as a predetermined level classified based on a predetermined criterion, wherein the basic physical condition output procedure includes the first basic physical condition It is preferred estimation procedure or a configuration of outputting the level of the person basic physical condition estimated by the second basic physical condition estimation procedure.

The computer program of the present invention is a computer program that causes a computer as a biological state estimation device to execute a biological state estimation procedure for analyzing a biological signal of a person measured by a biological signal measurement device and estimating a biological state, As a biological state estimation procedure, the biological signal is analyzed, and fluctuations in the state of the biological regulatory functional element including an index caused by fluctuations highly related to brain function, autonomic nervous function, physical / mental fatigue, or sense are detected. A bioregulatory function element determination procedure for calculating a plurality of indicators shown at predetermined time intervals determined in advance and obtaining a time-series change thereof; and each determination of each bioregulatory function element in the biocontrol function element determination procedure A basic physical condition estimation procedure for estimating the basic physical condition of the person corresponding to the basic physical condition estimation time set longer than the time; A basic physical condition output procedure for outputting the basic physical condition level of the person estimated by the basic physical condition estimation procedure every time the predetermined basic physical condition estimation time elapses, and the basic physical condition estimation procedure Analyzing each time-series change regarding the state of each bioregulatory functional element obtained by the bioregulatory functional element determination procedure according to a preset priority, and in accordance with a predetermined standard, the predetermined basic physical condition It is a procedure for estimating the basic physical condition of the person at the estimated time.

The basic physical condition estimation procedure of the computer program includes a time-series change of the bioregulatory function element having a high priority among the time-series changes of the bioregulatory function element obtained by the biocontrol function element determination procedure. A first basic physical condition estimation procedure for estimating the basic physical condition of the person at the predetermined basic physical condition estimation time as a predetermined level when the predetermined standard is satisfied; and the first basic physical condition estimation procedure If the subject is not subject to estimation in the first basic physical condition estimation procedure, the time series change relating to the other bioregulatory function elements having lower priority than the bioregulatory function elements used in the first basic physical condition estimation procedure is used, Performing a second basic physical condition estimation procedure for estimating physical condition as a predetermined level classified based on a predetermined criterion, wherein the basic physical condition output procedure includes the first physical condition output procedure. It is preferable that the foundation basis physical condition estimation procedure or configured to output a level of the person basic physical condition as determined by the second basic physical condition estimation procedure.

The first basic physical condition estimation procedure of the computer program includes an index highly relevant to the autonomic nervous function among the plurality of biological regulatory function elements determined in the biological regulatory function element determination procedure, or the body A configuration that uses a time-series change of an index highly related to mental fatigue and estimates the basic physical condition level of the person as “good” or “bad” when the time-series change satisfies a predetermined standard It is preferable that
When the second basic physical condition estimation procedure of the computer program is not estimated as “good” or “bad” in the first basic physical condition estimation procedure, Among the bioregulatory functional elements, from the time-series change of the index highly relevant to the sense, the level of the basic physical condition of the person is either “good”, “bad” or their “intermediate state”. A configuration to be estimated is preferable.

According to the present invention, each of a plurality of indicators indicating fluctuations in the state of the bioregulatory functional element, including indicators caused by fluctuations highly related to brain function, autonomic nervous function, physical / mental fatigue, or sense, Basic physical condition estimation time that is set longer than the determination time of each bioregulatory functional element by calculating for each set predetermined determination time, obtaining its time series change, and analyzing each time series change according to priority The physical condition can be obtained from the fluctuation performance that is the basis of the human adjustment system. In other words, in order to estimate the basic physical condition based on the time-series changes required for each of a plurality of bioregulatory functional elements having a contradictory nature within a predetermined basic physical condition estimation time, the priority order is statistically determined for each fluctuation performance. The basic physical condition level at the basic physical condition estimation time, for example, “good” or “bad” is estimated using the indicators of the bioregulatory functional elements according to the priority order, and further used for this estimation. If the basic physical condition cannot be classified by the bioregulatory function, the level of the basic physical condition corresponding to the disturbance of the regulation system, for example, `` Estimate and output “good”, “bad”, and their “intermediate state”.

Therefore, in the present invention, not only the determination result of the biological regulation function element (biological regulation function element) involved according to the physical condition level is output separately, but also using the judgment result of each biological regulation function element, Aside from the determination result of each bioregulatory functional element, the basic physical condition is estimated and output from the homeostasis maintenance function with fluctuations, and the driver and the manager etc. have the basic in the past 30 minutes or 1 hour etc. At a glance. Therefore, by accumulating this information, the driver or the like can make a self-determination that it is better to take a break. Of course, it is preferable to use the warnings based on the judgment results of the individual individual bioregulatory functional elements or to give priority to those warnings. Acts (such as warnings from administrators) can be performed more appropriately.

FIG. 1A is an exploded view showing an example of a biological signal measuring apparatus for measuring a back body surface pulse wave used in one embodiment of the present invention, and FIG. FIG. FIG. 2 is a diagram schematically showing the configuration of the biological state estimation apparatus according to one embodiment of the present invention. FIG. 3A is a diagram showing an example of a frequency slope time series waveform obtained by the zero cross method and the peak detection method obtained by the frequency slope time series waveform calculating means, and FIG. FIG. 3A shows a waveform obtained by performing the smoothing process. FIG. 4 is a diagram showing an example of a time-series waveform of the distribution rate obtained by the distribution rate calculating means. FIG. 5 is a diagram showing an example of a fatigue curve obtained by the fatigue curve calculation means. FIG. 6 is a diagram showing an example of a time series waveform of the homeostatic function level obtained by the homeostatic function level calculating means. FIG. 7A is a diagram showing an example of a physical condition map obtained by the physical condition map calculating means, and FIG. 7B is a diagram showing an example of a sensory map obtained by the sensory map calculating means. FIG. 8 is a flowchart for explaining a basic physical condition estimating step by the basic physical condition estimating means. FIGS. 9A and 9B are diagrams showing experimental results in a certain month of subjects A and B in Experimental Example 2. FIG. FIG. 10 is a diagram showing time-series data of the physical condition estimation result by the biological state estimation device on the 14th and 15th days of the subject B shown in FIG. 9B and the operation start time on the operation days before and after that. . FIG. 11 is a distribution diagram in which the correlation between the subjective evaluation of all subjects in Experimental Example 2 and the proportion of “good” in the physical condition estimation by the biological state estimation device is normalized.

Hereinafter, the present invention will be described in more detail based on the embodiments of the present invention shown in the drawings. Examples of the biological signal collected in the present invention include fingertip volume pulse wave, sound / vibration information collected from the back (hereinafter referred to as “back sound / vibration information”), and preferably back sound / vibration. Information. As described above, the back sound / vibration information is sound / vibration information generated from the motion of the heart and aorta detected from the upper back of a person, and includes information on ventricular systole and diastole, blood circulation It includes elasticity information of the blood vessel wall serving as an auxiliary pump, elasticity information based on blood pressure, and reflected wave information, that is, back body surface wave (APW) and pseudo heart sound information. The signal waveform associated with heart rate variability includes sympathetic and parasympathetic nervous system activity information (parasympathetic activity information including the compensation of sympathetic nerves), and the signal waveform associated with aortic oscillation is sympathetic. Since it contains information on neural activity and information on the endocrine system, it is suitable for determining bioregulatory functional elements from different viewpoints.

A fingertip plethysmograph can be used as a biological signal measuring device for collecting a biological signal as long as it is a fingertip plethysmogram. For example, a pressure sensor can be used as long as it is back sound / vibration information. Preferably, a living body used in a drowsy driving warning device (Sleep Buster (registered trademark)) manufactured by Delta Touring Co., Ltd. A signal measuring device 1 is used. FIG. 1 shows a schematic configuration of the biological signal measuring apparatus 1. This biological signal measuring apparatus 1 can be used by being incorporated in a driver's seat of a vehicle, and can collect a biological signal without restraining fingers.

The biological signal measuring apparatus 1 will be briefly described. As shown in FIGS. 1A and 1B, the first layer 11, the second layer 12, and the third layer 13 are stacked in order from the upper layer side. The first layer 11 made of a layer structure and made of a three-dimensional solid knitted fabric or the like is used by being positioned on the human body side that is a detection target of a biological signal. Therefore, sound / vibration information of the heart / vascular system including biological signals from the back of the trunk of the human body, in particular, biological sounds generated by vibrations of the ventricle, the atrium, and the large blood vessels (direct trunk sound or bioacoustic signal). (Back body surface pulse wave (including APW)) is first propagated to the first layer 11 which is a biological signal input system. The second layer 12 functions as a resonance layer that emphasizes the biological signal propagated from the first layer 11, particularly the sound / vibration of the heart / vascular system by a resonance phenomenon or a beat phenomenon, and a casing 121 made of a bead foam or the like. And a three-dimensional solid knitted fabric 122 that functions as a natural vibrator, and a film 123 that generates membrane vibration. A microphone sensor 14 is provided in the second layer 12 to detect sound / vibration information. The third layer 13 is laminated on the opposite side of the first layer 11 via the second layer 12 to reduce external sound / vibration input.

Next, the configuration of the biological state estimating device 100 of the present embodiment will be described with reference to FIG. The biological state estimation device 100 includes a biological adjustment functional element determination unit 200, a basic physical condition estimation unit 300, and a basic physical condition output unit 400. The biological state estimation apparatus 100 is configured by a computer (including a microcomputer) and performs a biological state estimation procedure that functions as the biological adjustment functional element determination unit 200, the basic physical condition estimation unit 300, and the basic physical condition output unit 400. A computer program that causes a computer to execute a bioregulatory function element determination procedure, a basic physical condition estimation procedure, and a basic physical condition output procedure is set in the storage unit.

Note that the computer program may be stored in a recording medium. If this recording medium is used, the program can be installed in the computer, for example. Here, the recording medium storing the program may be a non-transitory recording medium. The non-transitory recording medium is not particularly limited, and examples thereof include a recording medium such as a flexible disk, a hard disk, a CD-ROM, an MO (magneto-optical disk), a DVD-ROM, and a memory card. It is also possible to install the program by transmitting it to the computer through a communication line.

In the present embodiment, the biological adjustment functional element determination unit 200 analyzes the back sound / vibration information, which is a biological signal measured by the biological signal measuring device 1 described above, and is used for estimation of a basic physical condition of a person. A plurality of types of indices related to the functional elements are calculated at predetermined time intervals that are set in advance, and their time series changes are obtained. Note that the biological signal to be analyzed may be a fingertip volume pulse wave or the like, which is not excluded by the present invention, but the back sound / vibration information is preferable as described above.

A plurality of types of bioregulatory function elements determined by the bioregulatory function element determining means 200 are not limited, but at least an index highly relevant to brain function and autonomic nerve function, and relation to physical / mental fatigue It is preferable that the index includes a highly sensitive index and an index highly related to sense. These are indicators that show fluctuations in EEG fluctuations that affect human homeostasis functions, or how bioregulatory functions represented by body temperature control functions work. This is because the influence is large.

Examples of indices highly relevant to brain function and autonomic nerve function include time-series waveforms of frequency gradients obtained by processing collected biological signals, and the distribution ratios of the three signals described in the above section of the prior art. Examples include time-series waveforms, fatigue curves, and time-series fluctuations in the determination of the homeostasis maintenance function level. The time-series waveform of the frequency gradient shows fluctuations that are at the base of the regulation of the homeostatic function, and the fluctuations are well-balanced to balance the function of anti-trait, Relevance is particularly high. This is supported by statistical methods. The time-series waveform of each frequency band obtained from the distribution rate corresponds to the types of brain waves (θ waves, α waves, β waves) that are indirectly related to fluctuation rhythms, and functions in human brain function and autonomic nerve function. In addition, it is highly related to the regulatory function of the endocrine system. Since homeostasis is maintained by various regulatory systems such as the endocrine system and the autonomic nervous system, fluctuations in the level are deeply related to the regulatory performance due to fluctuations in the brain, autonomic nervous system and endocrine system.

The bioregulatory function element determining means 200 is a frequency slope time series waveform computing means for obtaining a time series waveform of a frequency slope as a computing means for obtaining an index that captures how the fluctuations of the brain function, autonomic nervous function and endocrine system fluctuate. 210, a distribution rate calculating means 220 for obtaining a distribution rate, a fatigue curve calculating means 230 for obtaining a fatigue curve, and a constancy maintaining function level calculating means 240 for obtaining a constancy maintaining function level.

The frequency gradient time series waveform calculation means 210 is a time series of frequencies from the time series waveform of the back body surface pulse wave (APW) near 1 Hz obtained by filtering the back sound / vibration information obtained from the sensor 14 of the biological signal measuring apparatus 1. After obtaining the waveform, the time-series waveform of the frequency is slid to calculate the frequency-gradient time-series waveform (see FIGS. 3A and 3B). The frequency gradient time series waveform calculation means 210 is a point (zero cross) that switches from positive to negative in the time series waveform of the back body surface pulse wave (APW) as disclosed in the above-mentioned Patent Document 1 by the present inventors. Point method) (zero-cross method) and a method of obtaining a time-series waveform using a maximum value (peak point) by smoothing and differentiating the time-series waveform of the back body surface pulse wave (APW) (peak detection method) There are two ways.

In the zero cross method, when the zero cross point is obtained, it is divided every 5 seconds, for example, and the reciprocal of the time interval between the zero cross points of the time series waveform included in the 5 second is obtained as the individual frequency f. The average value of the individual frequency f is adopted as the value of the frequency F for 5 seconds. Then, by plotting the frequency F obtained every 5 seconds in time series, a time series waveform of frequency fluctuation is obtained.

In the peak detection method, the maximum value of the time series waveform of the back body surface pulse wave (APW) is obtained by, for example, the smoothing differential method using Savitzky and Golay. Next, for example, the local maximum value is divided every 5 seconds, the reciprocal of the time interval between the local maximum values of the time-series waveform included in the 5 seconds is obtained as the individual frequency f, and the average value of the individual frequency f in the 5 seconds is calculated This is adopted as the value of the frequency F for 5 seconds. Then, by plotting the frequency F obtained every 5 seconds in time series, a time series waveform of frequency fluctuation is obtained.

The frequency gradient time-series waveform computing means 210 has a predetermined overlap time (for example, 18 seconds) and a predetermined time width (for example, 180 seconds) from a time-series waveform of frequency fluctuations obtained by the zero cross method or the peak detection method. A time window is set, a frequency gradient is obtained for each time window by the method of least squares, and a time series waveform of the gradient is output. This slide calculation is sequentially repeated to output the APW frequency gradient time-series change as a frequency gradient time-series waveform.

The dorsal body surface wave (APW) is a biological signal mainly including the state of control of the heart, which is the central system, that is, the state of sympathetic innervation of the artery, and the appearance information of the sympathetic nervous system and the parasympathetic nervous system. The frequency-gradient time-series waveform obtained by the zero-cross method (the waveform indicated as “0x” in FIGS. 3A and 3B) is more related to the state of control of the heart, and the appearance of the sympathetic nerve Although reflecting the state, the time-series waveform of the frequency gradient obtained by the peak detection method (the waveform indicated as “Peak” in FIGS. 3A and 3B) is more related to the heartbeat variability. Therefore, in order to grasp the state of the autonomic nervous function more clearly, it is preferable to use a frequency gradient time series waveform obtained by using the zero cross method.

Sympathetic nerve activity affects vascular elasticity and diameter, and the influence of reflected waves from the vascular wall is simulated heart sound information (detected from the back) included in sound and vibration information detected from the human back. Therefore, between a pseudo I sound (corresponding to a heart sound I sound) and a pseudo II sound (corresponding to a heart sound II sound) detected by a muscle, skin, etc. between the heart and the back surface. It is superimposed on the waveform component. This is the reason why the width between the zero cross points in the zero cross method differs from the width between the peak points in the peak detection method, and the zero cross method has a period affected by the reflected wave. Therefore, it is possible to capture sympathetic nerve information by looking at the frequency gradient time-series waveform by the zero cross method.
On the other hand, the peak value reflects the information of heart rate variability as described above, but the heart rate variability is mainly controlled by the parasympathetic nerve. Therefore, parasympathetic information can be captured by looking at the peak value.

The frequency gradient time series waveform obtained by the zero-cross method obtained by the frequency gradient time series waveform calculation means 210 has an amplitude that increases with a temporary increase in sympathetic nerve activity that occurs as resistance to sleepiness at a predetermined timing before sleep. It is known that when the tendency to change is shown, it can be regarded as an index of the predictive sleep phenomenon (see Patent Document 4). In addition, after a waveform showing a sleep onset symptom appears, the waveform shows a tendency to converge, and after that, when the fluctuation fluctuation of a long period is larger, the point at which the fluctuation of the long period starts to show the imminent sleep immediately before falling asleep. It is known that it can be regarded as an index indicating a phenomenon.

Distribution rate calculation means 220 first analyzes the frequency gradient time-series waveforms obtained from frequency gradient time-series waveform calculation means 210, respectively, and is lower than 0.0033 Hz, which is the frequency at which the characteristics of fluctuations in the cardiovascular system are switched. Each frequency component belonging to the VLF band is extracted from the ULF band corresponding to the frequency function adjustment signal, the fatigue acceptance signal having a frequency higher than the function adjustment signal, and the activity adjustment signal having a frequency higher than the fatigue acceptance signal. Next, the distribution ratios of these frequency components are obtained in time series. That is, the ratio of each frequency component when the sum of the power spectrum values of the three frequency components is 1 is obtained as a distribution rate in time series (see FIG. 4).

In the present embodiment, as shown in FIG. 4, a frequency component of 0.0017 Hz is used as the function adjustment signal, a frequency component of 0.0035 Hz is used as the fatigue acceptance signal, and a frequency component of 0.0053 Hz is used as the activity adjustment signal. Used. In atrial fibrillation, which is one of the heart diseases, the frequency at which the fluctuation characteristics of the heart and circulatory system are switched is said to be 0.0033 Hz. By capturing fluctuations in the vicinity of 0.0033 Hz, Information on activities and homeostasis can be obtained. The frequency bands below 0.0033 Hz and below 0.0053 Hz are mainly related to body temperature regulation, and the frequency band from 0.01 to 0.04 Hz is said to be related to autonomic nerve control. Yes. And when the inventors actually obtained a frequency gradient time-series waveform for calculating these low-frequency fluctuations inherent in the biological signal and analyzed it, 0.0017 Hz, which is a lower frequency than 0.0033 Hz, It was confirmed that there was a fluctuation in the frequency band centered at 0.0035 Hz in the vicinity of 0.0033 Hz and a fluctuation in the frequency band centered at 0.0053 Hz in addition to these two. However, the frequency component of each signal can be adjusted by individual differences and the like, the function adjustment signal is less than 0.0033 Hz, preferably 0.001 to 0.0027 Hz, and the fatigue acceptance signal is 0. In the range of 002 to 0.0052 Hz, the activity adjustment signal can be adjusted and used in the range of 0.004 to 0.007 Hz.

As shown in Patent Document 2, the time series change of the distribution ratio obtained by the distribution ratio calculation means 220 is, for example, a rapid decrease in the distribution ratio of 0.0017 Hz and a rapid increase in the distribution ratio of 0.0053 Hz. The time point showing the change can be regarded as the present time point of the impending sleep phenomenon.

The fatigue curve calculation means 230 is a means disclosed in Japanese Patent Application Laid-Open No. 2009-22610 by the present inventors, calculates an integral value by performing absolute value processing on a frequency gradient time series waveform obtained by the zero cross method, This integrated value is a means for obtaining the fatigue curve as shown in FIG. 5 by obtaining the integrated value as the degree of fatigue at every predetermined judgment time and plotting it corresponding to the time. The muscle activity is muscle contraction or relaxation, and the fatigue curve, which is the integrated information of the frequency gradient time series waveform by the zero cross method reflecting the information of the sympathetic nerve, is highly correlated with the muscle activity (Non-patent Document 1). reference). Therefore, in the fatigue curve, the point where the slope fluctuates more than a predetermined value indicates a singular point, and each singular point corresponds to the increasing fatigue and the point indicating that muscle activity has occurred and the blood flow increased. Shows the point.

The homeostasis maintenance function level determination unit 240 is based on the technique disclosed in Patent Document 3, and the positive / negative of the differential waveform of each frequency gradient time series waveform obtained by the frequency gradient time series waveform calculation unit 210 using the zero cross method. At the time of each frequency slope obtained by absolute value processing of the positive and negative of the integral waveform obtained by integrating the frequency slope time series waveform, the frequency slope time series waveform using the zero cross method and the frequency slope time series waveform using the peak detection method. The determination is made using at least one of the absolute values of the series waveform. Based on these combinations, the level of the homeostasis maintenance function is determined. For example, using a frequency gradient and an integral value, if the value is equal to or greater than a predetermined value, it is determined as “constancy maintaining function level 1”, or the differential value is equal to or less than a predetermined position and “ In the case of “peak advantage”, it can be set to be determined as “constancy maintaining function level 4”. And, for example, when the above conditions are variously combined to correlate with the human condition and judged as level 1 to 3, the normal to good condition is judged as level 4 to 6. Is determined to be necessary. Further, when it is determined that a sign of sleep onset or a sign of imminent sleep has occurred, an index such as levels 7 to 11 is assigned as a level that requires immediate warning depending on each state. In the product name “Sleep Buster” manufactured by Delta Touring Co., Ltd., the determination result by the homeostasis maintenance function level determination means 240 is set to be displayed as shown in FIG. 6, for example.

As an index highly relevant to physical / mental fatigue, a physical condition map and a sensory map, which are indices disclosed in Patent Document 2, can be used. These are graphs showing how fluctuations fluctuate, and show high relevance to human physical and mental fatigue.

Therefore, the biological adjustment functional element determination unit 200 of the present embodiment further includes a physical condition map calculation unit 250 and a sensory map calculation unit 260. The back body surface pulse wave (APW) obtained from the back sound / vibration information acquired from the biological signal measuring apparatus 1 is frequency-analyzed, and the analysis waveform is displayed on the logarithmic axis display for the target analysis section. Divided into low frequency band, medium frequency band, and high frequency band, the analysis waveform is scored based on a certain standard from the slope of the analysis waveform and the overall shape of the analysis waveform, and plotted on the coordinate axis It is. The physical condition map is a state in which the state of control of the autonomic nervous system is viewed as a balance between the sympathetic nerve and the parasympathetic nerve, and the sensory map is obtained by superimposing the state of change of heartbeat variability on the physical condition map.

Specifically, the physical condition map calculation means 250 first obtains a regression line for each predetermined period region with respect to the analysis waveform obtained by frequency analysis of the back body surface pulse wave. Next, each regression line obtained for each periodic area is given a region score based on its slope, and the difference in power spectral density value between regression lines in the adjacent frequency domain and the difference in slope between regression lines Based on the above, the number of break points indicating the branching phenomenon in each regression line is obtained, a shape score based on the number of break points is given, and at least one of the area score and the shape score is used to determine a determination reference point for each analysis waveform. Ask. As the area score, the slope of each regression line in each area is divided into three, approximately horizontal state, upward and downward, for example, increasing or decreasing the score in the upward direction and downward direction on the basis of the score in the approximately horizontal state To give a score. As the shape score, the smaller the number of break points, the higher the score.

When obtaining the determination reference point, the first determination reference point is obtained using the frequency slope time series waveform obtained by the zero cross method, and the second judgment reference is obtained using the frequency slope time series waveform obtained by the peak detection method. Find a point. Then, the coordinate point is plotted with the index based on the first determination reference point on one axis and the index based on the second determination reference point on the other axis, and the physical condition map as illustrated in FIG. Is created. In the physical condition map, the coordinate time-series change line connecting the coordinate points is determined to be comfortable when it is determined that the change trend approximates the 1 / f slope, and is determined to be changing vertically. If it is determined that it is uncomfortable. In FIG. 7A, a plurality of coordinate points are connected without being aligned with the coordinate origin, but when the change tendency of two points that are temporally different is viewed, the first point is aligned with the coordinate origin and the second point is When plotted in the fourth quadrant, this bioregulatory functional element is “good”, and determination becomes easier.

The sensory map calculation means 260 performs a movement calculation to obtain an average value of the frequency for each predetermined time window set with a predetermined overlap time in the time series waveform of the frequency using the peak detection method related to the heartbeat fluctuation, The time series change of the average value of the frequency obtained for each time window is obtained as a frequency fluctuation time series waveform, and an index corresponding to the functional point obtained from the time series waveform of the frequency using the zero cross method is taken on one axis. In addition, an index corresponding to the amount of change in the predetermined time width of the frequency fluctuation time series waveform obtained by the peak detection method is taken on the other axis, and the time series change of the coordinates obtained from the functional point and the amount of change is obtained. It is a way to go. FIG. 7B is an example of the sensory map obtained in this way. In FIG. 7B, a plurality of coordinate points are connected without being aligned with the coordinate origin. However, when the change tendency of two points that are different in time is observed, the first point is aligned with the coordinate origin and 2 points are obtained. When the eyes are plotted, it becomes easy to determine the separation distance and the separation direction between the two.

In addition, the functional point is between the determination reference points of the analysis waveform in the two time ranges before and after the comparison target:
Functional point = Judgment reference point for the later time range + (Judgment reference point for the later time range-Judgment reference point for the previous time range) x n (where n is a correction coefficient),
Is required.

As an index highly relevant to the sense, among the time-series changes of the homeostatic function level determined by the homeostatic function level determination unit 240, for example, normal to good using a frequency slope and an integral value It can be determined by using the frequency of the frequency (levels 1 to 3 in the above example) and the level index requiring attention (levels 4 to 6 in the above example) using their frequency. The homeostasis maintenance function level is highly related to the state of autonomic nervous function as described above, but when sympathetic decompensation acts on fatigue due to physical condition, basic physical strength, or motivation, fatigue There are times when you feel a feeling and sometimes you don't feel it. Therefore, the sympathetic compensation effect on fatigue and the basic physical condition are highly related to the sense of feeling it as fatigue. In addition, the sense here is a sensation similar to feeling of loss accompanied by fatigue or hypoxia.

The basic physical condition estimating unit 300 calculates the basic physical condition of the person to be analyzed based on a predetermined reference from each time series change regarding the fluctuation performance of each biological regulatory function element obtained by the biological regulatory function element determination unit 200. It is a means to estimate (basic physical condition). In the biological adjustment functional element determination unit 200, as described above, a plurality of types of time-series changes regarding the fluctuation performance of the biological adjustment functional elements are obtained. It is obtained every predetermined determination time. For example, the frequency gradient time-series waveform calculation unit 210 takes several minutes after obtaining the data from the biological signal measuring apparatus 1 until the first calculation result is output, but thereafter, for example, is obtained every 18 seconds. As a result, a time series change is required. The distribution rate obtained by the distribution rate computation means 220, the fatigue degree obtained by the fatigue curve computation means 230, and the homeostasis maintenance function level obtained by the homeostasis maintenance function level computation means 240 are also numbers until the first computation result is output. It takes minutes, and is obtained every 18 seconds, for example, and a time series change is obtained for each. The calculation results obtained by the physical condition map calculation means 250 and the sensation map calculation means 260 each take 20 to 30 minutes at first, but the second point is obtained about ten minutes later, and the third and subsequent points are obtained every few minutes. . On the other hand, the basic physical condition estimation means 300 estimates the basic physical condition for a time (basic physical condition estimation time) that is longer than each determination time in each bioregulatory function element.

For example, when the driver knows his / her physical condition, it is of course possible to display the determination results of the fluctuation performance of each bioregulatory functional element individually on the monitor, but in this case, the analysis of the driver who analyzes the data Variations are likely to occur depending on the ability and judgment ability. Therefore, it is preferable to estimate the basic physical condition by absorbing the variation by mechanically determining and statistically processing the combination of them in consideration of the contribution rate as necessary. That is, it is a method for estimating the current basic control ability (physical condition) of a living body by statistically combining data output every relatively short time. The determination results of each bioregulatory functional element are suitable for grasping the latest mental and physical state of the driver that changes sequentially, but even if these determination results are sequentially output during the operation of driving, In some cases, it is difficult for the driver himself to analyze the condition and become aware of the basic physical condition. Therefore, the basic physical condition estimation means 300 is configured to estimate the basic physical condition in the basic physical condition estimation time set to a longer time. The basic physical condition estimation time can be set arbitrarily, such as 15 minutes, 30 minutes, 60 minutes, etc., but if the frequency is too high and there are too many changes in the state, it will result in the driver's difficulty in grasping the physical condition. The time is preferably about 20 to 40 minutes.

The basic physical condition estimation means 300 determines the basic physical condition using the determination result of each bioregulatory functional element. Specifically, the determination result of the bioregulatory function element that more reflects the physical condition in the basic physical condition estimation time set to a predetermined long time is preferentially used. Therefore, in this embodiment, the priority order using the determination result of each bioregulatory functional element is set in the basic physical condition estimation procedure which is a computer program that configures the basic physical condition estimation means 300. More specifically, as shown in FIG. 2, the basic physical condition estimating means 300 includes a first basic physical condition estimating means 310 and a second basic physical condition estimating means 320, which are shown in the flowchart of FIG. First, the basic physical condition is estimated by the first basic physical condition estimating means 310 (S110, S120), and then the basic physical condition is estimated by the second basic physical condition estimating means 320 (S130). Is set to

The first basic physical condition estimating means 310 obtains whether or not the time-series change of the bioregulatory functional elements with higher priority among the bioregulatory functional elements with priority set satisfies a predetermined criterion, When the level is satisfied, the basic physical condition is estimated as the predetermined level. The second basic physical condition estimating means 320 is the first basic physical condition estimating means when the first basic physical condition estimating means is not subject to determination, that is, when the predetermined basic condition in the first basic physical condition estimating means 310 is not satisfied. A basic physical condition is estimated on the basis of a predetermined criterion by using a time-series change regarding another bioregulatory function element having a lower priority than the bioregulatory function element used in the means 310. Accordingly, since the second basic physical condition estimating means 320 is the final means for estimating the basic physical condition, the second basic physical condition estimating means 320 always classifies the data to be determined into any basic physical condition. It is set.

As the priority of each bioregulatory functional element obtained by the bioregulatory function element determining unit 200 to be estimated in the basic physical condition estimating unit 300, a number of cases are analyzed statistically as in the experimental examples described later, It is preferable to determine in consideration of the contribution ratio as necessary. Thereby, the estimation accuracy can be further improved. In this embodiment, based on statistical processing in an experimental example to be described later, one of the priorities of an index highly related to autonomic nervous function or a time series change of an index highly related to physical / mental fatigue. It is set to be high, and the next priority is set to be a time-series change of an index highly relevant to the senses. Therefore, in the first basic physical condition estimation means 310, whether or not the time series change of the index highly relevant to the autonomic nervous function satisfies a predetermined condition, the index highly relevant to the physical / mental fatigue is determined. It is set so as to determine whether or not the time series change satisfies a predetermined condition. In addition, when the time-series change of an index highly related to autonomic nervous function satisfies a predetermined condition, the basic physical condition is estimated to be “bad”, and the index is highly related to physical and mental fatigue. When the series change satisfies a predetermined condition, the basic physical condition is set to be “good”.

The above frequency gradient time series waveform, distribution rate, fatigue curve (fatigue level), and homeostasis maintenance function level, which are indices related to homeostasis maintenance function based on fluctuations in brain function, autonomic nervous function or endocrine system regulation function are It is an index that makes it easy to discern signs that occur as a result of fatigue accumulation, such as sleep onset, imminent sleep, and low-level driving. In addition, fluctuations in the homeostatic function that is regulated by brain function vary depending on the frequency band, and the regulated system governed by the endocrine system, etc., but the distribution rate among these is the regulation system. It is an index that reflects well when there is sudden change, decay and increase. Therefore, if the situation that requires caution or warning when performing operations such as driving using these appears prominently in the basic physical condition estimation time or the frequency is more than the predetermined, the basic physical condition is `` bad '' Estimated. The physical condition map / sensory map, which is an index highly related to physical / mental fatigue, can easily distinguish the index when feeling well and comfortable. Therefore, when this index is a condition that indicates smoothness and comfort, the basic physical condition is estimated as “good”. In the estimation of the basic physical condition in the first basic physical condition estimating means 310, an index highly relevant to the brain function, the autonomic nervous function and the regulatory function of the endocrine system and an index highly relevant to physical / mental fatigue There is no restriction on which one to use preferentially, but because there is basically a relationship with autonomic nerves in the modulation of physical and mental fatigue, as in this embodiment, brain function, autonomic nerve function, etc. It is preferable to perform estimation using an index highly relevant to human health, and then to perform estimation using an index highly relevant to physical and mental fatigue.

In the present embodiment, as described above, the brain function, the autonomic nervous function, and the endocrine function are highly relevant to the hormone secretion regulation function. As described above, the frequency gradient time series waveform, distribution rate, fatigue curve (fatigue degree), and constant There are four sex maintenance function levels. In one of these, it is possible to set the basic physical condition to be estimated as “bad” when a sign such as a sleep onset sign is detected above a predetermined level. In this case, it is preferable to estimate the basic physical condition as “bad” in order to increase the reliability.

Therefore, the first basic physical condition estimating unit 310 of the present embodiment includes a frequency gradient time-series waveform obtained from the frequency gradient time-series waveform computing unit 210, a time series waveform of the distribution rate obtained from the distribution rate computing unit 210, and a fatigue curve. When three or more indices satisfy a predetermined standard among the fatigue curve (time series waveform of fatigue level) obtained from the computing means 230 and the homeostasis maintenance function level obtained from the homeostasis maintenance function level computation means 240 ( When “Yes” is determined in S110 of FIG. 8), “bad” is estimated.

In the present embodiment, the predetermined criterion for estimating “bad” is set as follows.
(A) Index obtained from frequency gradient time series waveform calculation means 210 In the frequency gradient time series waveform using the zero cross method, the amplitude change is compared, and it is 1 in succession (usually set in the range of 2 to 4 times). When a converging point that changes to less than 90-60% of the previous amplitude occurs (an index that estimates that sympathetic nerve activity has fallen and cannot sleep well)
(B) Index obtained from the distribution rate calculating means 220 In the time series change of the distribution rate of the frequency gradient time series waveform using the zero cross method, 0 in a predetermined time range (usually set in the range of 60 to 120 seconds). When the distribution rate at .0017 Hz suddenly decreases (usually set at a reduction rate of 15% or more) and the distribution rate at 0.0053 Hz suddenly increases (usually set at an increase rate of 15% or more) during this period ( Estimated metrics)
(C) Index obtained from fatigue curve calculation means 230 Fatigue curve using peak detection method (frequency gradient time series waveform using peak detection method) during a predetermined time (usually set in the range of 3 to 10 minutes) Where the slope of the absolute value integration time series waveform) changes more significantly than the slope of the fatigue curve using the zero-cross method (frequency slope time series waveform integration of the frequency slope time-series waveform using the zero-cross method) When the fatigue curve using the peak detection method reaches a predetermined value or more when a predetermined time elapses (an index that estimates that the parasympathetic nerve activity is extremely dominant)
(D) Index obtained from homeostasis maintenance function level calculating means 240 Of the homeostasis maintenance function levels obtained every 18 seconds, a level lower than the normal level, in the above example, several times of caution judgments of levels 4-6 Appears more than a dozen times (an index that appears when parasympathetic activity is presumed to be dominant), or a level that requires a warning, in the above example, a warning judgment of levels 7 to 11 appears several times (An index that appears when a sudden increase or a drastic decrease in sympathetic nerve activity is estimated)

Further, the first basic physical condition estimating means 310 of the present embodiment provides data that is not estimated as “bad” in three or more of the indicators (a) to (d) (“No” in S110 of FIG. 8). With respect to (determined data), it is estimated whether or not it corresponds to “good” satisfying a predetermined criterion by using the indices of the physical condition map calculating means 250 and the sensory map calculating means 260 (S120 in FIG. 8).
(E) Index when it is determined that the basic physical condition is “good” As a standard corresponding to the basic physical condition “good”, in this embodiment, the time series change obtained from the physical condition map calculation unit 250 is The point at which the calculation result is output (as described above, the first point and the second point are output after a predetermined time has passed, but the third and subsequent points are output every few minutes) is aligned with the coordinate origin. In this case, when the next point is plotted in the fourth quadrant and the time series change obtained from the sensory map calculation means 260 is set to the coordinate origin, the previous point is also more than a predetermined value in the X-axis direction. It is set to be estimated as “good” when plotted at a distance (in the case of “Yes” in S120 of FIG. 8).

Note that the criteria (a) to (d) for which the basic physical condition is estimated to be “bad” and the criterion (e) for which the basic physical condition is estimated to be “good” are statistics for a number of cases described later. It is based on a statistical analysis, but is not limited to this. For example, data may be accumulated for each individual, and the condition may be set statistically for each individual.

The second basic physical condition estimating means 320, when the data to be estimated does not satisfy both the “bad” and “good” criteria in the first basic physical condition estimating means 310 (“No” in S110 of FIG. 8). And when “No” is determined in S120). The second basic physical condition estimating means 320 is an index of a level that can be said to be normal to good with sympathetic nerve activity among time series changes in the homeostasis maintenance function level obtained by the homeostasis maintenance function level determination means 240 (the above-mentioned In the example, the appearance frequency of the level near the boundary between the levels 1 to 3) and the index of the level where parasympathetic nerve activity is dominant and requires attention (levels 4 to 6 in the above example) is compared. However, since the difference in state is small at one level difference, it is preferable to compare at two or more different levels. In the present embodiment, the ratio of the appearance frequency of the level 2 index, which is the central index among the three levels that can be said to be normal to good, is compared with the level 4 index that needs to be noted. Basically, it can be estimated as “good” when the frequency of appearance of level 2 showing superior state with high sympathetic nerve activity is high and the frequency of appearance of level 4 showing precaution state with low parasympathetic nerve activity is low, When the appearance frequency is opposite, it can be estimated as “bad”, but the data to be analyzed by the second basic physical condition estimating means 320 is clearly “good”, “ Since it was not estimated as “bad”, data that is difficult to classify is assumed. Therefore, in the present invention, in a test example to be described later, a large number of cases are analyzed, and a criterion for classifying them into “good”, “bad”, and “intermediate state” is set by a Bayesian estimation method. Details will be described later.

In this embodiment, the basic physical condition output unit 400 satisfies the above-described condition of the first basic physical condition estimation unit 310 (when “Yes” is determined in S110 of FIG. 8 or “Yes” in S120). When it is determined, the basic physical condition level is output as “bad” (S111 in FIG. 8) or “good” (S121 in FIG. 8) as the estimation result. When the condition of the first basic physical condition estimating means 310 is not satisfied (when “No” is determined in S110 of FIG. 8 and “No” is determined in S120), the second basic physical condition estimating means is used. “Good”, “bad”, and “intermediate state” of the estimation results 320 are output (S131 in FIG. 8). The basic physical condition output unit 400 outputs “good”, “bad”, or “intermediate state”, which is an estimation result of the basic physical condition, through a medium that can be recognized by a person. For example, if the biological signal measurement device 1 and the biological state estimation device 100 according to the present embodiment are mounted on an automobile and detect a driver's state, the in-vehicle monitor estimates the basic physical condition estimation time every time. Display the results. The display method may be characters or symbols. As a symbol, for example, the symbol “sunny” of the weather is used for “good”, and the symbol “rain” of the weather is used for “bad”. In the case of “intermediate state”, the symbol “cloudy” of weather can be used. In addition, a plurality of types of characters may be displayed, or these may be combined and displayed. It can also be output by voice via a vehicle-mounted speaker.

According to the present embodiment, for example, when analyzing the driver's condition, in addition to the change of each bioregulatory functional element for each short time, the basic physical condition of the driver is determined for each predetermined basic physical condition estimation time. It can be estimated and output. Therefore, it is easy for the driver to be aware of the state in which he / she has been driving, for example, for the last 30 minutes. For example, if “bad” is continuously estimated as a basic physical condition, It is easy to encourage decisions such as taking a break. In addition, by connecting the vehicle state estimation device 100 mounted on the vehicle and the computer on the manager side via communication means, not only the individual bioregulatory functional elements of the driver but also the basic physical condition in real time. Can be used to help managers make decisions.

(Experimental example 1)
Next, experimental results regarding the setting of the estimation standard of the first basic physical condition estimation unit 310 and the estimation standard of the second basic physical condition estimation unit 320 constituting the basic physical condition estimation unit 300 will be described.

(experimental method)
A biological signal estimation device 100 is mounted as a biological signal measurement device on a seat back of a driver's seat, and a biological state estimation device 100 which is a computer to which data of back sound and vibration information measured by the biological signal measurement device 1 is input is mounted. The subject was seated in a car that was being run. The biological state estimation device 100 uses a product name “Sleep Buster” manufactured by Delta Touring Co., Ltd.

The test subject was a healthy Japanese man in his 60's who performed continuous operation for about 40 minutes multiple times on different days. The subject self-reported his / her physical condition after driving. Self-assessment was made in five stages: “good”, “slightly good”, “normal”, “slightly bad”, and “bad”. In this embodiment, the ambiguous cases of “slightly good”, “normal”, and “slightly bad” are excluded from self-reports, and the driving data of 55 cases that the subject has clearly determined as “good” and “bad”. Compared with the estimation results of basic physical condition by the method of.

I. Estimation of basic physical condition by the first basic physical condition estimating means 310 (1) Examination of whether or not the index obtained from the frequency gradient time-series waveform calculating means 210 satisfies the above condition (a) The condition of (a) is: The chi-square test was performed by setting whether or not the convergence point of the waveform that changed to less than 90% of the previous amplitude in two successive occurrences occurred at least once and compared with the subjectivity of the subject. The 2 × 2 cross table is shown in the following table, p = 0.24, and the correct answer rate was 67%.

(2) Examination of whether or not the index obtained from the distribution rate calculating means 220 satisfies the above condition (b) The condition (b) is that the distribution rate at 0.0017 Hz decreases by 20% or more in 90 seconds, In the meantime, it was set when the distribution rate of 0.0053 Hz increased by 20% or more, and a chi-square test was performed in comparison with the subjectivity of the subject. The 2 × 2 cross table is shown in the following table, but p = 0.11 and the correct answer rate was 69%.

(3) Examination of whether or not the index obtained from the fatigue curve calculation means 230 satisfies the above condition (c) The condition (c) is a range of 6 minutes, and the slope of the fatigue curve using the peak detection method is There is one or more places that change to more than 1.5 times the slope of the fatigue curve using the zero cross method, and the fatigue curve using the peak detection method is greater than 0.03 after 30 minutes The chi-square test was performed in comparison with the subjectivity of the subjects. The 2 × 2 cross table is shown in the following table, p = 0.19, and the correct answer rate was 64%.

(4) Examination of whether or not the index obtained from the constancy maintaining function level calculating means 240 satisfies the above condition (d). The condition (d) is determined as levels 4 to 6 in the above example in 6 minutes. It was set when the judgment of level 7 to 11 appeared 10 times or more, or chi-square test compared with the subjectivity of the subject. The 2 × 2 cross table is shown in the following table, p = 0.018, and the correct answer rate was 71%.

The following table summarizes the numbers that satisfy the conditions (a) to (d) in the 55 cases of travel data.

As is clear from Table 5, all seven cases where three or more of the conditions (a) to (d) are satisfied are evaluated as “bad” in the subjectivity of the subject. Therefore, in the first basic physical condition estimating means 310, the frequency slope time series waveform obtained from the frequency slope time series waveform computing means 210, the time series waveform of the distribution rate obtained from the distribution rate computing means 210, and the fatigue curve computing means 230. Among the fatigue curves (time series waveform of the fatigue level) obtained from the above and the homeostasis maintenance function level obtained from the homeostasis maintenance function level calculation means 240, three or more indicators are respectively the above (a) to (d) It is preferable to set so that it is estimated as “bad” when the above condition is satisfied.

(5) Examination of whether or not the above condition (e) is satisfied that the basic physical condition is determined to be “good” For all the 55 cases of travel data, the physical condition map calculation means 250 obtains the first determination result from the start of measurement. It was examined whether or not the coordinate point of the second determination result was plotted in the fourth quadrant when the coordinate origin was set. Similarly, the coordinate point of the second determination result is set to a predetermined scale in the positive direction of the X axis when the first determination result from the start of measurement is matched with the coordinate origin by all of the 55 cases of travel data. Above (in this experiment example, since the separation distance of many cases (23 cases) out of 38 cases of subjective evaluation “good” was scale 5 or more, set the scale 5 or more) I investigated. When the calculation results of the physical condition map calculation unit 250 and the sensory map calculation unit 260 both satisfy the above condition, the first basic physical condition estimation unit 310 has an estimation result that the basic physical condition is “good”. The results of chi-square test compared with subjectivity are shown in the following table. p = 0.095 and the correct answer rate was 56%.

In the examination of Table 6 above, the running data of all 55 cases are used. As shown in the flowchart of FIG. 8, the first basic physical condition estimating means 310 first performs the above (a) to (d The basic physical condition is estimated to be “bad” when three or more of the above conditions are satisfied (S110), and the case where it is not estimated to be “bad” corresponds to “good” based on the physical condition map and sensory map. Is determined (S120). Therefore, when 48 cases excluding 7 cases satisfying 3 or more of the above conditions (a) to (d) were compared, the following results were obtained. From this table, when 7 cases were excluded, p = 0.032 and the correct answer rate was 56%. It is preferable to perform “good” estimation when p <0.05 and the condition for “defective” estimation is not satisfied.

II. Estimation of basic physical condition by second basic physical condition estimating means 320 Of the homeostatic function level calculated by the homeostatic function level calculating means 240 of the biological state estimating apparatus 100, the total number of appearances of levels 1 to 6 is set to 1. The appearance ratio of level 2 indicating a state in which sympathetic nerve activity is dominant and level 4 indicating a state in which parasympathetic nerve activity is dominant is obtained. These were performed for all 55 cases, and the following probability table was created using Bayesian estimation.

The basic physical condition estimated by the second basic physical condition estimating means 320 using the probability table of Table 8 is as shown in the following table.

When examining Table 8 and Table 9 above, the running data of all 55 cases are used to create a probability table. However, as shown in the flowchart of FIG. If the basic physical condition is not estimated by the first basic physical condition estimating means 310, the physical condition is estimated. Accordingly, the following table shows the results of comparative examination of 29 cases excluding 26 cases estimated as “good” or “bad” by the first basic physical condition estimating means 310.

As shown in the flowchart of FIG. 8, the biological state estimation apparatus 100 according to the present embodiment first estimates the “bad” state in the first basic physical condition estimation unit 310 for the estimation target data (S110). When it is not estimated as “bad”, the state of “good” is estimated (S120), and when neither of them is estimated, the second basic physical condition estimating means uses the Bayes estimation probability table shown in Table 8 Based on this, it is estimated whether it is “good”, “bad”, or “intermediate state” (indicated in the above table as “intermediate”) (S130). 55 cases evaluated as “good” or “bad” are used.

Therefore, in these 55 cases, the data finally estimated as “intermediate” in the biological state estimation device 100 does not match the subjective evaluation of the subject. Therefore, while excluding the “intermediate” data (11 cases), the first basic physical condition estimation means 310 estimates “bad” (failure: 7 cases), and excludes the “bad” data. The second basic physical condition estimating means except for the data estimated as “good” (good: 19 cases) and the data estimated as “bad” and “good” in the first basic physical condition estimating means 310. In 320, the data estimated as “bad” and “good” (bad: 7 cases, good: 11 cases), that is, the estimation results finally output by the basic physical condition output means 400 for the data without duplication. The table below summarizes the results of the subjective evaluation of the subjects.

According to Table 11, p = 1.58 × 10 ⁻⁷ , the correct answer rate is 91%, and the estimation result of the basic physical condition by the biological state estimation device 10 of the present embodiment is high with the subject's subjective evaluation. It can be seen that the reliability of the estimation result of the basic physical condition of the present embodiment is high.

Of the 55 cases, except for the data estimated as “good” or “bad” by the first basic physical condition estimating means 310, 11 cases estimated as “intermediate” by the second basic physical condition estimating means 320. The data does not match the subject's subjective assessment, but this is thought to depend on the subject's basic physical strength and tendency to think. That is, in the case of the subject of this license example, since there is a strong tendency of positive thinking and physical strength, most of the 11 cases are considered to be self-reported subjectively as “good”.

(Experimental example 2)
Next, a driver's truck's driver's seat is equipped with a sleep buster (registered trademark) manufactured by Delta Touring Co., Ltd. as a biological signal measuring device 1, and a biological signal of a professional driver is collected. The biological condition estimation apparatus 100 in which each estimation criterion shown in FIG. 1 is set, the analysis according to the flowchart shown in FIG. 8 is performed, and the physical condition of the subject every 30 minutes is expressed as “good”, “intermediate”, “ Estimated in three stages of “bad”. The subject professional drivers were seven men from their 20s to 50s, and the analysis was conducted on the total number of operations 71 times of all subjects. In addition, each subject reported his / her subjective physical condition on the day of operation in five stages from “abnormal” (level: 1) to “excellent” (level: 5) after the operation ended.

In addition, "the number of times of operation" is one time (one operation) from the operation start time to the operation end time of the subject work day on each subject's work day (there may be a date on the calendar such as night shift). It counts and includes the continuous driving for several hours and the rest time between them. On the other hand, the estimation result by the biological state estimation apparatus 100 is output once every about 30 minutes. Therefore, when a continuous operation of one hour or more is included in one operation, the biological state estimation device 100 can obtain a plurality of estimation results. On the other hand, the subjective evaluation of each subject on the day of operation is a single declaration at the end of one operation.

FIGS. 9A and 9B show the results for a certain month of subjects A and B, and the ratio (“good”) of the total number of physical condition estimations during one operation performed by the biological state estimation device 100 ( The vertical axis on the right side of each graph) and the subjective physical condition evaluation (the vertical axis on the left side of each graph) after the end of the five-day operation of the subject are plotted by time of operation and shown in time series.

9 (a) and 9 (b), the data of any subject approximates the increase / decrease tendency of the ratio change determined to be “good” by the biological state estimation apparatus 100 and the tendency of the subjective evaluation change. I understand that. Table 12 shows the result of the physical condition estimation and subjective evaluation of the subject A in comparison with the previous day (previous operation), where the proportion of the physical condition estimation “good” has increased in comparison with the previous day. In addition, “+” indicates that the subjective evaluation is good, and “−” indicates that the percentage of the physical condition estimation “good” is decreasing and the subjective evaluation is poor, both of which are the previous day. In the same case, “±” is displayed. In addition, the case where the increasing / decreasing tendency is indicated by a circle.

Table 12 suggests that subjectivity and physical condition estimation tend to be similar except when the subjective evaluation is the same as the previous day.

Table 13 summarizes the results of all subjects compared with the previous day (previous operation) similar to Table 12, and the coincidence rate of increase / decrease trend of all seven subjects is 0.71. As a result of Fisher's exact test, p = 0.032. As a result, a significant relationship was found between the increase and decrease in the proportion of “good” in the physical condition estimation by the biological state estimation device 100 and the subjective evaluation. In addition, the number of data for which the subjective evaluation was “bad” when the percentage of “good” in physical condition estimation was “increased” was significantly small, and the subjective evaluation was when the percentage of “good” in physical condition estimation was “decreased” The number of “bad” data was significantly larger. On the other hand, the number of responses that the subjective evaluation was the same as the previous day's operation was 32 out of 71 operations, and 38 responses were “normal (level: 3)” during 71 operations. It was also suggested that it is relatively difficult to be aware of exactly.

Here, in the data of the subject B in FIG. 9B, pay attention to the operation on the 14th and 15th, when the subjective evaluation is 1 (abnormal). FIG. 10 shows the time series data of the physical condition estimation result by the biological state estimation device 100 on the 14th and 15th day of the subject B and the operation start time from the 8th to the 16th. The operation on the 14th is 15:48 at the start of operation and the end of the operation is past 3:00 am on the 15th. The operation on the 15th is at 18:46 at the start of the operation and on the 16th in the morning. It was past 3 o'clock.

On the 14th, the physical condition estimation result goes back and forth between “good” and “bad”, and “bad” estimation continues at the end. On the 14th, since subject B responded that light sleepiness occurred at the end of the operation in the questionnaire after operation, it changed from early morning / day work until 11th to afternoon / night work after 14th. In addition, since the 12th and 13th were holidays, it was presumed that the body was not sufficiently adapted or switched to the work mode or night shift mode, and drowsiness or poor physical condition was felt. The 15th is almost “bad” in the first half, but “good” is also estimated in the second half. : Corresponds to the change to 2 (malfunction). Therefore, it can be said that the physical condition estimation result by the biological state estimation device 100 of the present embodiment estimates a state close to the subject's subjective evaluation, and is output once every about 30 minutes so that the subject can It is also suggested that changes in physical condition that are difficult to recognize can be reflected.

FIG. 11 is a distribution diagram in which the correlation between the subjective evaluation of all subjects and the proportion of “good” in the physical condition estimation by the biological state estimation apparatus 100 is normalized, and a linear approximation of the distribution is obtained for each subject and shown by a thin line. Yes. In FIG. 11, the data located on the right side of the broken line obliquely drawn by 45 degrees indicates that the proportion of the physical condition estimation result “good” is higher than the subjective evaluation, and the data located on the left side of the broken line is compared with the subjective evaluation. This shows that the rate of physical condition estimation result “good” is low. In addition, the linear approximation line of each subject indicates that the closer the slope is to 45 degrees, the higher the positive correlation between the subjective evaluation and the physical condition estimation result, and the lower the slope is to 0 degrees, the lower the correlation is. A negative value indicates a high negative correlation. Since there are three levels of subjective evaluation, “good”, “intermediate”, and “bad”, the physical condition estimation results by the biological state estimation device 100 tend to spread in the horizontal direction with respect to the subjective evaluation. However, it shows a generally positive slope, and FIG. 11 also suggests that there is a certain degree of correlation between the physical condition estimation by the biological state estimation device 100 and the subjective evaluation.

In the experimental example described above, the analysis is performed using the computer program set in the biological state estimation device 100 which is an in-vehicle computer and the data stored in the storage unit. Of course, it is possible to set the operation manager's computer and perform the same analysis as described above. In this case, the analysis target data is transmitted from the in-vehicle biological state estimation apparatus 100 to the operation manager's computer via a communication line (wireless or the like) so that the operation manager's computer can also analyze the data in real time. Alternatively, after the end of the driving operation, the data stored in the in-vehicle biological state estimation device 100 may be taken out and analyzed afterward on the operation manager's computer. By collecting such data, the operation manager can grasp the situation at the time of driving for each driver, and can also use it to give advice for more appropriate driving.

DESCRIPTION OF SYMBOLS 1 Biosignal measuring apparatus 11 Core pad 12 Spacer pad 13 Sensor 100 Living body state estimation apparatus 200 Bioregulation function element determination means 210 Frequency inclination time series waveform calculation means 220 Distribution rate calculation means 230 Fatigue curve calculation means 240 Constancy maintenance function level calculation means 250 physical condition map calculating means 260 sensory map calculating means 300 basic physical condition estimating means 310 first basic physical condition estimating means 320 second basic physical condition estimating means

Claims

A biological state estimation device for analyzing a biological signal of a person measured by a biological signal measurement device and estimating a biological state,
Analyzing the biological signal, a plurality of indicators showing fluctuations in the state of the bioregulatory functional element, including an indicator caused by fluctuations highly related to brain function, autonomic nervous function, physical / mental fatigue or sensation, A bioregulatory function element determining means for calculating a time-series change by calculating each predetermined determination time set in advance;
Basic physical condition estimation means for estimating the basic physical condition of the person corresponding to the basic physical condition estimation time set longer than each determination time of each of the biological adjustment functional elements in the biological adjustment functional element determination means;
Basic physical condition output means for outputting the basic physical condition level of the person estimated by the basic physical condition estimation means at every elapse of the predetermined basic physical condition estimation time;
The basic physical condition estimating means analyzes each time series change related to the state of each of the bioregulatory function elements obtained by the bioregulatory function element determining means according to a preset priority order, in light of a predetermined standard, The biological state estimating device, characterized in that it is means for estimating the basic physical condition of the person at the predetermined basic physical condition estimation time.
The basic physical condition estimating means is:
Among the time-series changes related to each bioregulatory function element obtained by the bioregulatory function element determination means, the time-series change of the bioregulatory function element having a high priority satisfies a predetermined criterion. First basic physical condition estimating means for estimating the basic physical condition of the person at a basic physical condition estimation time as a predetermined level;
When the first basic physical condition estimating means is not an estimation target, the time series change relating to another biological regulatory function element having a lower priority than the biological regulatory function element used in the first basic physical condition estimating means is performed. Using a second basic physical condition estimating means for estimating the basic physical condition of the person as a predetermined level classified based on a predetermined standard;
The living body state estimation apparatus according to claim 1, wherein the basic physical condition output means outputs the level of the basic physical condition of the person estimated by the first basic physical condition estimation means or the second basic physical condition estimation means. .
The first basic physical condition estimating means includes an index highly relevant to the autonomic nerve function or the physical / mental fatigue among the plurality of biological adjustment functional elements determined by the biological adjustment functional element determination means. The time series change of a highly relevant index is used, and when the time series change satisfies a predetermined standard, the basic physical condition level of the person is estimated as “good” or “bad”. Biological state estimation device.
The first basic physical condition estimating means estimates the basic physical condition of the person as “bad” when a time-series change of an index highly relevant to the autonomic nervous function satisfies a predetermined criterion, The biological state according to claim 3, which is a means for estimating the basic physical condition of the person as "good" when a time-series change of the index highly related to physical and mental fatigue satisfies a predetermined standard. Estimating device.
The second basic physical condition estimation means includes a plurality of the biological adjustment functional elements determined by the biological adjustment functional element determination means when the first basic physical condition estimation means does not estimate "good" or "bad" The basic physical condition level of the person is estimated as either “good”, “bad”, or “intermediate state” based on a time-series change of an index highly relevant to the sense. The biological state estimation apparatus according to any one of 2 to 4.
A biological state estimation method for analyzing a biological signal of a person measured by a biological signal measuring device using a computer and estimating a biological state,
Analyzing the biological signal, a plurality of indicators showing fluctuations in the state of the bioregulatory functional element, including an indicator caused by fluctuations highly related to brain function, autonomic nervous function, physical / mental fatigue or sensation, A bioregulatory functional element determination procedure for calculating a time-series change by calculating each predetermined determination time set in advance;
A basic physical condition estimation procedure for estimating a basic physical condition of the person corresponding to a basic physical condition estimation time set longer than each determination time of each of the biological adjustment functional elements in the biological adjustment functional element determination procedure;
A basic physical condition output procedure for outputting the basic physical condition level of the person estimated by the basic physical condition estimation procedure at every elapse of the predetermined basic physical condition estimation time;
The basic physical condition estimation procedure analyzes each time series change related to the state of each bioregulatory functional element obtained by the bioregulatory functional element determination procedure according to a preset priority, and in light of a predetermined standard, The biological state estimation method, which is a procedure for estimating the basic physical condition of the person at the predetermined basic physical condition estimation time.
The basic physical condition estimation procedure includes:
Among the time-series changes related to each bioregulatory function element obtained by the bioregulatory function element determination procedure, when the time-series change of the bioregulatory function element having a high priority satisfies a predetermined criterion, the predetermined A first basic physical condition estimation procedure for estimating the basic physical condition of the person at a basic physical condition estimation time as a predetermined level;
When not subject to estimation in the first basic physical condition estimation procedure, the time-series change relating to another biological regulation function element having a lower priority than the biological regulation function element used in the first basic physical condition estimation procedure. Using a second basic physical condition estimation procedure to estimate the basic physical condition of the person as a predetermined level classified based on a predetermined criterion;
The biological condition estimation method according to claim 6, wherein the basic physical condition output procedure outputs a level of the basic physical condition of the person estimated by the first basic physical condition estimation procedure or the second basic physical condition estimation procedure. .
In a computer as a biological state estimation device,
A computer program for analyzing a biological signal of a person measured by a biological signal measuring device and executing a biological state estimation procedure for estimating a biological state,
As the biological state estimation procedure,
Analyzing the biological signal, a plurality of indicators showing fluctuations in the state of the bioregulatory functional element, including an indicator caused by fluctuations highly related to brain function, autonomic nervous function, physical / mental fatigue or sensation, A bioregulatory functional element determination procedure for calculating a time-series change by calculating each predetermined determination time set in advance;
A basic physical condition estimation procedure for estimating a basic physical condition of the person corresponding to a basic physical condition estimation time set longer than each determination time of each of the biological adjustment functional elements in the biological adjustment functional element determination procedure;
A basic physical condition output procedure for outputting the basic physical condition level of the person estimated by the basic physical condition estimation procedure for each elapse of the predetermined basic physical condition estimation time;
The basic physical condition estimation procedure analyzes each time series change related to the state of each bioregulatory functional element obtained by the bioregulatory functional element determination procedure according to a preset priority, and in light of a predetermined standard, A computer program which is a procedure for estimating a basic physical condition of the person at the predetermined basic physical condition estimation time.
The basic physical condition estimation procedure includes:
Among the time-series changes related to each bioregulatory function element obtained by the bioregulatory function element determination procedure, when the time-series change of the bioregulatory function element having a high priority satisfies a predetermined criterion, the predetermined A first basic physical condition estimation procedure for estimating the basic physical condition of the person at a basic physical condition estimation time as a predetermined level;
When not subject to estimation in the first basic physical condition estimation procedure, the time-series change relating to another biological regulation function element having a lower priority than the biological regulation function element used in the first basic physical condition estimation procedure. Using a second basic physical condition estimation procedure to estimate the basic physical condition of the person as a predetermined level classified based on a predetermined criterion;
The computer program according to claim 8, wherein the basic physical condition output procedure outputs the level of the basic physical condition of the person determined by the first basic physical condition estimation procedure or the second basic physical condition estimation procedure.
In the first basic physical condition estimation procedure, among the plurality of bioregulatory function elements determined in the bioregulatory function element determination procedure, an index highly relevant to the autonomic nervous function or the physical / mental fatigue The time series change of a highly relevant index is used, and when the time series change satisfies a predetermined standard, the level of the basic physical condition of the person is estimated as “good” or “bad”. Computer program.
When the second basic physical condition estimation procedure is not estimated as “good” or “bad” in the first basic physical condition estimation procedure, the plurality of biological regulation function elements determined in the biological regulation function element determination procedure The basic physical condition level of the person is estimated as either “good”, “bad”, or “intermediate state” based on a time-series change of an index highly relevant to the sense. The computer program according to 9 or 10.